{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/absolute-position-encodings/papers/125","list_of":"/method/absolute-position-encodings","method":"Absolute Position Encodings","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":125,"pages_in_order":140,"rows_per_page":100,"rows":[12401,12500],"of":13942,"counts":{"archive_papers_tagged":13942,"with_a_code_link":6505,"where_syntology_ran_a_sample":2224,"not_listed_spam_title":0,"listed":13942,"listed_where_code_ran":2224,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1897,"every_run_a_failure_of_syntologys_instrument":327,"listed_with_a_run_with_no_instrument_failure":1897,"listed_every_run_a_failure_of_syntologys_instrument":327,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/absolute-position-encodings","prev":"/method/absolute-position-encodings/papers/124","next":"/method/absolute-position-encodings/papers/126","papers":[{"paper":"/paper/updet-universal-multi-agent-reinforcement","slug":"updet-universal-multi-agent-reinforcement","title":"UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers","date":"2021-01-20","arxiv_id":"2101.08001","n_code_links":1,"syntology":null},{"paper":"/paper/fast-convergence-of-detr-with-spatially","slug":"fast-convergence-of-detr-with-spatially","title":"Fast Convergence of DETR with Spatially Modulated Co-Attention","date":"2021-01-19","arxiv_id":"2101.07448","n_code_links":2,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["gaopengcuhk/SMCA-DETR"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/dual-level-collaborative-transformer-for","slug":"dual-level-collaborative-transformer-for","title":"Dual-Level Collaborative Transformer for Image Captioning","date":"2021-01-16","arxiv_id":"2101.06462","n_code_links":1,"syntology":null},{"paper":"/paper/match-ignition-plugging-pagerank-into","slug":"match-ignition-plugging-pagerank-into","title":"Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching","date":"2021-01-16","arxiv_id":"2101.06423","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploration-of-visual-features-and-their","title":"Exploration of Visual Features and their weighted-additive fusion for Video Captioning","date":"2021-01-14","arxiv_id":"2101.05806","n_code_links":0,"syntology":null},{"paper":null,"slug":"privacy-analysis-in-language-models-via","title":"Training Data Leakage Analysis in Language Models","date":"2021-01-14","arxiv_id":"2101.05405","n_code_links":0,"syntology":null},{"paper":"/paper/coarse-and-fine-grained-hostility-detection","slug":"coarse-and-fine-grained-hostility-detection","title":"Coarse and Fine-Grained Hostility Detection in Hindi Posts using Fine Tuned Multilingual Embeddings","date":"2021-01-13","arxiv_id":"2101.04998","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-news-recommendation-with-negative","title":"Neural News Recommendation with Negative Feedback","date":"2021-01-12","arxiv_id":"2101.04328","n_code_links":0,"syntology":null},{"paper":"/paper/bert-gt-cross-sentence-n-ary-relation","slug":"bert-gt-cross-sentence-n-ary-relation","title":"BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer","date":"2021-01-11","arxiv_id":"2101.04158","n_code_links":0,"syntology":null},{"paper":null,"slug":"investigating-the-vision-transformer-model","title":"Investigating the Vision Transformer Model for Image Retrieval Tasks","date":"2021-01-11","arxiv_id":"2101.03771","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-mahalanobis-distance-for","slug":"revisiting-mahalanobis-distance-for","title":"Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection","date":"2021-01-11","arxiv_id":"2101.03778","n_code_links":1,"syntology":null},{"paper":null,"slug":"spherical-transformer-adapting-spherical","title":"Spherical Transformer: Adapting Spherical Signal to CNNs","date":"2021-01-11","arxiv_id":"2101.03848","n_code_links":0,"syntology":null},{"paper":null,"slug":"channel-boosting-feature-ensemble-for-radar","title":"Channel Boosting Feature Ensemble for Radar-based Object Detection","date":"2021-01-10","arxiv_id":"2101.03531","n_code_links":0,"syntology":null},{"paper":"/paper/deep-reinforcement-learning-with-function","slug":"deep-reinforcement-learning-with-function","title":"Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies","date":"2021-01-09","arxiv_id":"2101.03418","n_code_links":1,"syntology":null},{"paper":"/paper/trankit-a-light-weight-transformer-based","slug":"trankit-a-light-weight-transformer-based","title":"Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing","date":"2021-01-09","arxiv_id":"2101.03289","n_code_links":1,"syntology":null},{"paper":"/paper/leveraging-multilingual-transformers-for-hate","slug":"leveraging-multilingual-transformers-for-hate","title":"Leveraging Multilingual Transformers for Hate Speech Detection","date":"2021-01-08","arxiv_id":"2101.03207","n_code_links":1,"syntology":null},{"paper":"/paper/compound-word-transformer-learning-to-compose","slug":"compound-word-transformer-learning-to-compose","title":"Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs","date":"2021-01-07","arxiv_id":"2101.02402","n_code_links":5,"syntology":{"ran":4,"of":9,"n_ran_checked":4,"n_instrument":0,"unverified":5,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["YatingMusic/compound-word-transformer"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/trackformer-multi-object-tracking-with","slug":"trackformer-multi-object-tracking-with","title":"TrackFormer: Multi-Object Tracking with Transformers","date":"2021-01-07","arxiv_id":"2101.02702","n_code_links":2,"syntology":null},{"paper":"/paper/autodropout-learning-dropout-patterns-to","slug":"autodropout-learning-dropout-patterns-to","title":"AutoDropout: Learning Dropout Patterns to Regularize Deep Networks","date":"2021-01-05","arxiv_id":"2101.01761","n_code_links":1,"syntology":null},{"paper":"/paper/i-bert-integer-only-bert-quantization","slug":"i-bert-integer-only-bert-quantization","title":"I-BERT: Integer-only BERT Quantization","date":"2021-01-05","arxiv_id":"2101.01321","n_code_links":7,"syntology":null},{"paper":"/paper/transformers-and-transfer-learning-for","slug":"transformers-and-transfer-learning-for","title":"Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning","date":"2021-01-04","arxiv_id":"2101.01213","n_code_links":1,"syntology":null},{"paper":null,"slug":"transformers-in-vision-a-survey","title":"Transformers in Vision: A Survey","date":"2021-01-04","arxiv_id":"2101.01169","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-efficient-transformer-decoder-with","title":"An Efficient Transformer Decoder with Compressed Sub-layers","date":"2021-01-03","arxiv_id":"2101.00542","n_code_links":0,"syntology":null},{"paper":null,"slug":"analogical-reasoning-for-visually-grounded-1","title":"Analogical Reasoning for Visually Grounded Compositional Generalization","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ariel-volume-coding-for-sentence-generation-1","title":"AriEL: Volume Coding for Sentence Generation Comparisons","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-is-not-enough-mitigating-the","title":"Attention Is Not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"block-skim-transformer-for-efficient-question","title":"Block Skim Transformer for Efficient Question Answering","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cluster-former-clustering-based-sparse-1","title":"Cluster-Former: Clustering-based Sparse Transformer for Question Answering","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/crackformer-transformer-network-for-fine","slug":"crackformer-transformer-network-for-fine","title":"CrackFormer: Transformer Network for Fine-Grained Crack Detection","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/deep-representational-re-tuning-using","slug":"deep-representational-re-tuning-using","title":"Deep Representational Re-tuning using Contrastive Tension","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"discovering-human-interactions-with-large","title":"Discovering Human Interactions With Large-Vocabulary Objects via Query and Multi-Scale Detection","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"do-transformers-understand-polynomial","title":"Do Transformers Understand Polynomial Simplification?","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-detr-end-to-end-object-detection-with","title":"Dynamic DETR: End-to-End Object Detection With Dynamic Attention","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/event-based-video-reconstruction-using","slug":"event-based-video-reconstruction-using","title":"Event-Based Video Reconstruction Using Transformer","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-routing-strategies-for-multilingual","title":"Exploring Routing Strategies for Multilingual Mixture-of-Experts Models","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"frequency-aware-spatiotemporal-transformers","title":"Frequency-Aware Spatiotemporal Transformers for Video Inpainting Detection","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generalizing-tree-models-for-improving","title":"Generalizing Tree Models for Improving Prediction Accuracy","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"high-performance-discriminative-tracking-with","title":"High-Performance Discriminative Tracking With Transformers","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hypergrid-transformers-towards-a-single-model","title":"HyperGrid Transformers: Towards A Single Model for Multiple Tasks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/image-harmonization-with-transformer","slug":"image-harmonization-with-transformer","title":"Image Harmonization With Transformer","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-generalizability-of-protein","title":"Improving Generalizability of Protein Sequence Models via Data Augmentations","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-machine-translation-by-searching","title":"Improving Machine Translation by Searching Skip Connections Efficiently","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ketg-a-knowledge-enhanced-text-generation","title":"KETG: A Knowledge Enhanced Text Generation Framework","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"long-range-arena-a-benchmark-for-efficient","title":"Long Range Arena : A Benchmark for Efficient Transformers","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"memory-representation-in-transformer","title":"Memory Representation in Transformer","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-view-3d-reconstruction-with","title":"Multi-View 3D Reconstruction With Transformers","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-co-attention-transformer-for","slug":"multimodal-co-attention-transformer-for","title":"Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"non-iterative-parallel-text-generation-via","title":"Non-iterative Parallel Text Generation via Glancing Transformer","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"on-position-embeddings-in-bert","title":"On Position Embeddings in BERT","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"parameterization-of-hypercomplex","title":"Parameterization of Hypercomplex Multiplications","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/phrasetransformer-self-attention-using-local","slug":"phrasetransformer-self-attention-using-local","title":"PhraseTransformer: Self-Attention using Local Context for Semantic Parsing","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"post-training-weighted-quantization-of-neural","title":"Post-Training Weighted Quantization of Neural Networks for Language Models","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"predictive-attention-transformer-improving","title":"Predictive Attention Transformer: Improving Transformer with Attention Map Prediction","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"representation-and-bias-in-multilingual-nlp","title":"Representation and Bias in Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"representational-correlates-of-hierarchical","title":"Representational correlates of hierarchical phrase structure in deep language models","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/scene-context-aware-salient-object-detection","slug":"scene-context-aware-salient-object-detection","title":"Scene Context-Aware Salient Object Detection","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"share-or-not-learning-to-schedule-language","title":"Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"single-layers-of-attention-suffice-to-predict","title":"Single Layers of Attention Suffice to Predict Protein Contacts","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/star-a-structure-aware-lightweight","slug":"star-a-structure-aware-lightweight","title":"STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/subformer-a-parameter-reduced-transformer","slug":"subformer-a-parameter-reduced-transformer","title":"Subformer: A Parameter Reduced Transformer","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/subformer-exploring-weight-sharing-for","slug":"subformer-exploring-weight-sharing-for","title":"Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers","date":"2021-01-01","arxiv_id":"2101.00234","n_code_links":1,"syntology":null},{"paper":null,"slug":"synthesizer-rethinking-self-attention-for","title":"Synthesizer: Rethinking Self-Attention for Transformer Models","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"trans-caps-transformer-capsule-networks-with","title":"Trans-Caps: Transformer Capsule Networks with Self-attention Routing","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-protein-language-models-are","title":"Transformer protein language models are unsupervised structure learners","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-ql-a-step-towards-making","title":"Transformer-QL: A Step Towards Making Transformer Network Quadratically Large","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"transformers-satisfy","title":"Transformers satisfy","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"transforming-recurrent-neural-networks-with","title":"Transforming Recurrent Neural Networks with Attention and Fixed-point Equations","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/trar-routing-the-attention-spans-in","slug":"trar-routing-the-attention-spans-in","title":"TRAR: Routing the Attention Spans in Transformer for Visual Question Answering","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"updet-universal-multi-agent-rl-via-policy","title":"UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-transformers-where-do-transformers","title":"Visual Transformers: Where Do Transformers Really Belong in Vision Models?","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/visualsparta-sparse-transformer-fragment","slug":"visualsparta-sparse-transformer-fragment","title":"VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words","date":"2021-01-01","arxiv_id":"2101.00265","n_code_links":1,"syntology":null},{"paper":null,"slug":"wb-detr-transformer-based-detector-without","title":"WB-DETR: Transformer-Based Detector Without Backbone","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multi-modal-deep-learning-model-for-video","title":"A Multi-modal Deep Learning Model for Video Thumbnail Selection","date":"2020-12-31","arxiv_id":"2101.00073","n_code_links":0,"syntology":null},{"paper":"/paper/fully-non-autoregressive-neural-machine","slug":"fully-non-autoregressive-neural-machine","title":"Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade","date":"2020-12-31","arxiv_id":"2012.15833","n_code_links":1,"syntology":null},{"paper":"/paper/minilmv2-multi-head-self-attention-relation","slug":"minilmv2-multi-head-self-attention-relation","title":"MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers","date":"2020-12-31","arxiv_id":"2012.15828","n_code_links":2,"syntology":null},{"paper":null,"slug":"revisiting-robust-neural-machine-translation","title":"Revisiting Robust Neural Machine Translation: A Transformer Case Study","date":"2020-12-31","arxiv_id":"2012.15710","n_code_links":0,"syntology":null},{"paper":"/paper/transtrack-multiple-object-tracking-with","slug":"transtrack-multiple-object-tracking-with","title":"TransTrack: Multiple Object Tracking with Transformer","date":"2020-12-31","arxiv_id":"2012.15460","n_code_links":2,"syntology":null},{"paper":null,"slug":"verb-knowledge-injection-for-multilingual","title":"Verb Knowledge Injection for Multilingual Event Processing","date":"2020-12-31","arxiv_id":"2012.15421","n_code_links":0,"syntology":null},{"paper":null,"slug":"xlm-t-scaling-up-multilingual-machine","title":"XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders","date":"2020-12-31","arxiv_id":"2012.15547","n_code_links":0,"syntology":null},{"paper":"/paper/optimizing-deeper-transformers-on-small","slug":"optimizing-deeper-transformers-on-small","title":"Optimizing Deeper Transformers on Small Datasets","date":"2020-12-30","arxiv_id":"2012.15355","n_code_links":1,"syntology":null},{"paper":null,"slug":"transformer-for-image-quality-assessment","title":"Transformer for Image Quality Assessment","date":"2020-12-30","arxiv_id":"2101.01097","n_code_links":0,"syntology":null},{"paper":"/paper/unnatural-language-inference","slug":"unnatural-language-inference","title":"UnNatural Language Inference","date":"2020-12-30","arxiv_id":"2101.00010","n_code_links":1,"syntology":null},{"paper":"/paper/a-hierarchical-transformer-with-speaker","slug":"a-hierarchical-transformer-with-speaker","title":"A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in Conversation","date":"2020-12-29","arxiv_id":"2012.14781","n_code_links":1,"syntology":null},{"paper":"/paper/kaleidoscope-an-efficient-learnable-1","slug":"kaleidoscope-an-efficient-learnable-1","title":"Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps","date":"2020-12-29","arxiv_id":"2012.14966","n_code_links":2,"syntology":{"ran":16,"of":23,"n_ran_checked":9,"n_instrument":7,"unverified":7,"pointer_only":0,"phrase":"16 ran (of which 9 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 7 where Syntology's instrument failed) · 7 unverified","official":{"repos":["HazyResearch/butterfly","HazyResearch/learning-circuits"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":9,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/layoutlmv2-multi-modal-pre-training-for","slug":"layoutlmv2-multi-modal-pre-training-for","title":"LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding","date":"2020-12-29","arxiv_id":"2012.14740","n_code_links":9,"syntology":null},{"paper":"/paper/sit3-code-summarization-with-structure","slug":"sit3-code-summarization-with-structure","title":"Code Summarization with Structure-induced Transformer","date":"2020-12-29","arxiv_id":"2012.14710","n_code_links":1,"syntology":null},{"paper":"/paper/lattice-free-mmi-adaptation-of-self","slug":"lattice-free-mmi-adaptation-of-self","title":"Lattice-Free MMI Adaptation Of Self-Supervised Pretrained Acoustic Models","date":"2020-12-28","arxiv_id":"2012.14252","n_code_links":2,"syntology":null},{"paper":"/paper/red-dragon-ai-at-textgraphs-2020-shared-task","slug":"red-dragon-ai-at-textgraphs-2020-shared-task","title":"Red Dragon AI at TextGraphs 2020 Shared Task: LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking","date":"2020-12-28","arxiv_id":"2012.14164","n_code_links":1,"syntology":null},{"paper":"/paper/syntax-enhanced-pre-trained-model","slug":"syntax-enhanced-pre-trained-model","title":"Syntax-Enhanced Pre-trained Model","date":"2020-12-28","arxiv_id":"2012.14116","n_code_links":1,"syntology":null},{"paper":"/paper/transpose-towards-explainable-human-pose","slug":"transpose-towards-explainable-human-pose","title":"TransPose: Keypoint Localization via Transformer","date":"2020-12-28","arxiv_id":"2012.14214","n_code_links":1,"syntology":null},{"paper":"/paper/learning-light-weight-translation-models-from","slug":"learning-light-weight-translation-models-from","title":"Learning Light-Weight Translation Models from Deep Transformer","date":"2020-12-27","arxiv_id":"2012.13866","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":1,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["libeineu/GPKD"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"portfolio-optimization-with-2d-relative","title":"Portfolio Optimization with 2D Relative-Attentional Gated Transformer","date":"2020-12-27","arxiv_id":"2101.03138","n_code_links":0,"syntology":null},{"paper":null,"slug":"sg-net-syntax-guided-transformer-for-language","title":"SG-Net: Syntax Guided Transformer for Language Representation","date":"2020-12-27","arxiv_id":"2012.13915","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-hateful-memes-using-a-multimodal","slug":"detecting-hateful-memes-using-a-multimodal","title":"Detecting Hateful Memes Using a Multimodal Deep Ensemble","date":"2020-12-24","arxiv_id":"2012.13235","n_code_links":1,"syntology":null},{"paper":null,"slug":"i-like-fish-especially-dolphins-addressing","title":"I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling","date":"2020-12-24","arxiv_id":"2012.13391","n_code_links":0,"syntology":null},{"paper":null,"slug":"future-guided-incremental-transformer-for","title":"Future-Guided Incremental Transformer for Simultaneous Translation","date":"2020-12-23","arxiv_id":"2012.12465","n_code_links":0,"syntology":null},{"paper":null,"slug":"domain-adaptation-of-nmt-models-for-english","title":"Domain Adaptation of NMT models for English-Hindi Machine Translation Task at AdapMT ICON 2020","date":"2020-12-22","arxiv_id":"2012.12112","n_code_links":0,"syntology":null},{"paper":null,"slug":"molecular-ct-unifying-geometry-and","title":"Molecular CT: Unifying Geometry and Representation Learning for Molecules at Different Scales","date":"2020-12-22","arxiv_id":"2012.11816","n_code_links":0,"syntology":null},{"paper":"/paper/multi-head-self-attention-with-role-guided","slug":"multi-head-self-attention-with-role-guided","title":"Multi-Head Self-Attention with Role-Guided Masks","date":"2020-12-22","arxiv_id":"2012.12366","n_code_links":1,"syntology":null},{"paper":"/paper/3d-object-detection-with-pointformer","slug":"3d-object-detection-with-pointformer","title":"3D Object Detection with Pointformer","date":"2020-12-21","arxiv_id":"2012.11409","n_code_links":1,"syntology":null}],"record_sha256":"fc44a2f45f06162bd3bc3e190064bcb1221471dabae7e8f31d7a1efeb0a58bcf","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}